Search engines are expected to return to users the most relevant results for their requests which are submitted in the form of search string or search query. They are also supposed to return similar documents to a reference document supplied by the user. Search engines rely on two basic traditional methods: keyword matching and Pagerank. Keyword matching is based on information collected from inside the documents (and therefore content-related) and various analytics that are applied to it. Pagerank is based on counting the references (links) to each document across the Internet (like citation count which is common in the evaluation of the importance of essays in the academic world). Other methods used by search engines are contextual advertisement (advertisement based on keywords like AdWords and AdSense), clustering, image processing, voice recognition and statistics collected and performed on the search strings. Others use grammar rules. The results suffer from various limitations, in the quantitative and qualitative aspects. These problems can be summarized as follows:    1. False positives: Among the returned results some may be relevant, while others may be only partially relevant, and the rest are irrelevant.    2. Relevant results get low relevancy score and are therefore pushed down in the result lists and frequently ignored by the user.    3. Too many results which are not ranked correctly are returned and hide the relevant results from the user.
Current search engines are usually based on “containing” criteria when matching between tags (also known as keywords or metadata) and keywords in a search list. The terms in the search keyword list, set by the searcher, are compared to the keyword sets that are associated with the object (also known as tags, or metadata). More matched words yield higher ranks. All the words usually get the same weight. In order to increase the chances of matching with a wide variety of keywords that the searcher might use, a very common practice is to add more terms to keyword lists associated with the objects. In this process, keywords with very little relevancy are added to the keyword lists that are associated with objects. As a result, ranking objects that match the same tags/keywords in the search list, based on relevancy, is impossible with search engines that are based on tags matching only. Tag matching based on the “containing” criteria is very common for images, video clips and footage.